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compare: s478993:b8d9925e58f17a4b7079f2e31411e8c361375d60
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3 Commits
5fd844a69c ... b8d9925e58

Author SHA1 Message Date
Maciej Chmielarz b8d9925e58 Tworzenie pliku z wynikami 2023-10-24 18:04:12 +02:00
Maciej Chmielarz 14288b0a8c Merge branch 'master' of https://git.wmi.amu.edu.pl/s478993/fantastyczna_wycena 2023-10-24 18:00:35 +02:00
Maciej Chmielarz 1c515cecf7 Tworzenie plików z wynikami 2023-10-24 17:58:23 +02:00
3 changed files with 777 additions and 77 deletions
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242
SysInf.ipynb
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@ -16,7 +16,7 @@
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@ -1643,7 +1643,7 @@
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"[1763 rows x 4 columns]"
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@ -1905,7 +1905,7 @@
"max 2.318580e+06 6.000000 234.200000 2021.000000"
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@ -2092,7 +2092,7 @@
"[320 rows x 4 columns]"
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@ -2110,7 +2110,7 @@
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@ -2119,7 +2119,7 @@
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@ -2128,7 +2128,7 @@
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@ -2138,7 +2138,7 @@
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@ -2241,7 +2241,7 @@
"max 6.000000e+06 11.000000 404.000000 2022.000000"
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@ -2259,7 +2259,7 @@
},
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"execution_count": 36,
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@ -2275,7 +2275,7 @@
},
{
"cell_type": "code",
"execution_count": 45,
"execution_count": 37,
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"source": [
@ -2285,7 +2285,7 @@
},
{
"cell_type": "code",
"execution_count": 46,
"execution_count": 38,
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"source": [
@ -2295,7 +2295,7 @@
},
{
"cell_type": "code",
"execution_count": 47,
"execution_count": 39,
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"source": [
@ -2311,7 +2311,7 @@
},
{
"cell_type": "code",
"execution_count": 48,
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
@ -2320,7 +2320,7 @@
},
{
"cell_type": "code",
"execution_count": 49,
"execution_count": 41,
"metadata": {},
"outputs": [
{
@ -2332,7 +2332,7 @@
"LinearRegression()"
]
},
"execution_count": 49,
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
@ -2350,16 +2350,16 @@
},
{
"cell_type": "code",
"execution_count": 50,
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import mean_squared_error, r2_score"
"from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error"
]
},
{
"cell_type": "code",
"execution_count": 51,
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
@ -2368,7 +2368,7 @@
},
{
"cell_type": "code",
"execution_count": 52,
"execution_count": 44,
"metadata": {},
"outputs": [
{
@ -2377,7 +2377,7 @@
"0.6264197107092035"
]
},
"execution_count": 52,
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
@ -2388,7 +2388,7 @@
},
{
"cell_type": "code",
"execution_count": 53,
"execution_count": 45,
"metadata": {},
"outputs": [
{
@ -2397,7 +2397,7 @@
"22930468691.882328"
]
},
"execution_count": 53,
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
@ -2408,7 +2408,27 @@
},
{
"cell_type": "code",
"execution_count": 54,
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"74283.11238859448"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean_absolute_error(y_true=y_train, y_pred=y_train_pred)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
@ -2417,7 +2437,7 @@
},
{
"cell_type": "code",
"execution_count": 55,
"execution_count": 48,
"metadata": {},
"outputs": [
{
@ -2426,7 +2446,7 @@
"0.5872189061243203"
]
},
"execution_count": 55,
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
@ -2437,7 +2457,7 @@
},
{
"cell_type": "code",
"execution_count": 56,
"execution_count": 49,
"metadata": {},
"outputs": [
{
@ -2446,7 +2466,7 @@
"12603444942.321444"
]
},
"execution_count": 56,
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
@ -2455,6 +2475,26 @@
"mean_squared_error(y_true=y_val, y_pred=y_val_pred)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"68895.89161086899"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean_absolute_error(y_true=y_val, y_pred=y_val_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
@ -2464,7 +2504,7 @@
},
{
"cell_type": "code",
"execution_count": 57,
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
@ -2473,7 +2513,7 @@
},
{
"cell_type": "code",
"execution_count": 58,
"execution_count": 52,
"metadata": {},
"outputs": [
{
@ -2567,7 +2607,7 @@
"[292 rows x 1 columns]"
]
},
"execution_count": 58,
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
@ -2586,7 +2626,7 @@
},
{
"cell_type": "code",
"execution_count": 59,
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
@ -2602,7 +2642,7 @@
},
{
"cell_type": "code",
"execution_count": 60,
"execution_count": 54,
"metadata": {},
"outputs": [
{
@ -2633,7 +2673,7 @@
},
{
"cell_type": "code",
"execution_count": 61,
"execution_count": 55,
"metadata": {},
"outputs": [
{
@ -2664,7 +2704,7 @@
},
{
"cell_type": "code",
"execution_count": 62,
"execution_count": 56,
"metadata": {},
"outputs": [
{
@ -2695,7 +2735,7 @@
},
{
"cell_type": "code",
"execution_count": 63,
"execution_count": 57,
"metadata": {},
"outputs": [
{
@ -2726,7 +2766,7 @@
},
{
"cell_type": "code",
"execution_count": 64,
"execution_count": 58,
"metadata": {},
"outputs": [
{
@ -2757,7 +2797,7 @@
},
{
"cell_type": "code",
"execution_count": 65,
"execution_count": 59,
"metadata": {},
"outputs": [
{
@ -2788,7 +2828,7 @@
},
{
"cell_type": "code",
"execution_count": 66,
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
@ -2800,7 +2840,7 @@
},
{
"cell_type": "code",
"execution_count": 70,
"execution_count": 61,
"metadata": {},
"outputs": [
{
@ -2809,7 +2849,7 @@
"184531.94"
]
},
"execution_count": 70,
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
@ -2820,7 +2860,7 @@
},
{
"cell_type": "code",
"execution_count": 69,
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
@ -2829,6 +2869,54 @@
"filename = 'mieszkania_model'\n",
"pickle.dump(model, open(filename, 'wb'))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Zapisywanie wyników"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Zapisywanie wyników zbioru walidacyjnego"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [],
"source": [
"y_val_pred = pd.DataFrame(y_val_pred)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"y_val_pred.to_csv(r'dev-0\\out.tsv', sep='\\t', header=None, index=None)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Zapisywanie wyników zbioru testowego"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"y_test_pred_df.to_csv(r'test-A\\out.tsv', sep='\\t', header=None, index=None)"
]
}
],
"metadata": {

320
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